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Optimized Generalised Metric Learning Model for Iterative, Efficient, Accurate, and Improved Coronary Heart Diseases
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Author(s): P. Preethy Jemima (SRM Institute of Science and Technology, India), R. Gokul (SRM Institute of Science and Technology, India), R. Ashwin (SRM Institute of Science and Technology, India)and S. Matheswaran (SRM Institute of Science and Technology, India)
Copyright: 2024
Pages: 16
Source title:
Advanced Applications of Generative AI and Natural Language Processing Models
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Bharat Bhushan (School of Engineering and Technology, Sharda University, India), Muthmainnah S. (Universitas Al Asyariah Mandar, Indonesia)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-0502-7.ch018
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Abstract
Artificial intelligence (AI) is bringing about a revolution in the healthcare sector thanks to the growing availability of both structured and unstructured data, as well as the rapid advancement of analytical methodologies. Medical diagnosis models are essential to saving human lives; thus, we must be confident enough to treat a patient as advised by a black-box model. Concerns regarding the lack of openness and understandability, as well as potential bias in the model's predictions, are developing as AI's significance in healthcare increases. The use of neural networks as a classification method has become increasingly significant. The benefits of neural networks make it possible to classify given data effectively. This study uses an optimized generalized metric learning neural network model approach to examine a dataset on heart disorders. In the context of cardiac disease, the authors first conducted the correlation and interdependence of several medical aspects. A goal is to identify the most pertinent characteristics (an ideal reduced feature subset) for detecting heart disease.
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